Naive Bayes

Naive Bayes model with Gaussian, multinomial, or kernel
predictors

Naive Bayes models assume that observations have some multivariate
distribution given class membership, but the predictor or features
composing the observation are independent. This framework can accommodate
a complete feature set such that an observation is a set of multinomial
counts.

To train a naive Bayes model, use fitcnb in
the command-line interface. After training, predict labels or estimate
posterior probabilities by passing the model and predictor data to predict.

The naive Bayes classifier is designed for use when predictors are independent
of one another within each class, but it appears to work well in practice even
when that independence assumption is not valid.